Recently the active use of Internet and multimedia technologies has increased the violation of copyright. Digital watermarking has a crucial role in the field of multimedia copyright protection. In this paper, an optimized image watermarking technique based on Discrete Wavelet Transform (DWT) and Hadamard transform is proposed. Genetic Algorithm (GA) is an optimizing technique used to satisfy the tradeoff

Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average

Relations between objects drive our understanding of images. Modelling them poses several challenges due to the combinatorial nature of the problem and the complex structure of natural language. This paper tackles the task of predicting relationships in the form of (subject, relation, object) triplets from still images. To address these issues, we propose a framework for learning relation prototypes

Acoustic analysis of the speech signal enables non-intrusive, affordable, unbiased and fast assessment of voice pathologies. This assessment provides complimentary information to otolaryngologist for preliminary diagnosis of pathological larynx. Several voice impairment assessment systems focused on acoustic analysis have been introduced in recent years. Nevertheless, these systems are tested using

Person re-identification plays a central role in tracking and monitoring crowd movement in public places, and hence it serves as an important means for providing public security in video surveillance application sites. The problem of person re-identification has received significant attention in the past few years, and with the introduction of deep learning, several interesting approaches have been

The location and scale filters in discriminative correlation filter methods are lack of accurate rotation representation capability and updated with fixed intervals, which leads to tracking failure and time-consuming in complex scenarios. In this manuscript, a robust tracker integrating particle filter into correlation filter is presented to cope with sharp rotation and remarkable deformation. The

Person re-identification(Re-ID) has attracted increasing attention in the field of computer vision due to its great significance for the potential real-world applications. Profited from the success of convolutional neural networks(CNNs), existing multi-layer approaches leverage different scales of convolutional layers to learn more discriminative features, improving the Re-ID performance to some extent

In this paper, a novel denoising approach based on optimal trilateral filtering using Grey Wolf Optimization (GWO) is proposed. At first, a database of noisy images are generated by adding Gaussian noise, Salt & Pepper noise and Random noise to the captured image. The filtering of noisy images are performed by Block-matching and 3D filtering (BM3D) algorithm over the components of image obtained through

For service and multimedia processing that are not limited by time and space, it is necessary to go beyond the existing computing paradigm and resolve such limitations. In this study, health big data-based cardiac disease induction prediction made with multimedia extraction is suggested, which analyzes the relationships in health big data using multimedia extraction. Multimedia extraction is roughly

In the video surveillance of medical institutions, pain intensity is a significant clue to the state of patients. Of late, some approaches leverage various spatio-temporal methods to capture the dynamic pain information of videos for accomplishing pain estimation automatically. However, there is still a challenge in the spatio-temporal saliency, which means pain is always reflected in some important

Video files of surveillance cameras frequently play a role of major evidence for criminals or accidents. But, if the video file containing the evidence is deleted, we need to recover it if possible. In this paper, we propose a recovery scheme for deleted video files with AVI format on FAT32 file system. The proposed scheme first recovers deleted video files from unallocated storage space exploiting

Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method

Accurate recognition of green fruit targets is one of the key technologies for fruit growth monitoring and yield estimation. To solve the problem of fruit misidentification due to the similarity between fruit skin and leaf colors, a progressive detection method of green apples in natural environments was proposed. Image enhancement based on fuzzy set theory was carried out to make the fruit targets

The latest mobile devices such as smartphone, laptop, and tablet PC have supported IEEE 802.11 interfaces for broadband wireless data services. To extend the coverage in medium or large-scale areas such as office buildings, airports, campuses, or convention center, multiple access points (APs) need to be deployed. When a mobile station (MS) moves an AP to another, it typically experiences a long delay

Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated

To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing

Emotion recognition is a very important technique for effective interaction between human and artificial intelligence (AI) system. For a long time, facial expression-based methods have been actively studied, and they are showing high recognition performance thanks to powerful deep learning recently. On the other hand, the images of the datasets used in the conventional emotion recognition studies are

There are various face recognition techniques in literature, which are faced with challenges such as occlusion, pose variation, illumination, and facial expressions. Existing methods often perform well when their database is small, or multiple samples per person exist. However, face recognition methods with just one reference sample per person may not work well, especially on a large database. To address

One of the most ubiquitous cause of worldwide deforestation and devastation of wildlife is fire. To control fire and reach the forest area in time is not always possible. Consequently, the level of destruction is often high. Therefore, predicting fires well in time and taking immediate action is of utmost importance. However, traditional fire prediction approaches often fail to detect fire in time

Recently, image super-resolution has reinforced image resolution enhancement approaches in real-time and ensuring visual quality of super resolved images has evolved as a key research problem. Most quantitative benchmarks rely on full reference metrics which would work in the presence of a reference image. However, the unavailability of ground truth images in real world applications and the size constraints

Early classification on time series has emerged as an active research area in the field of machine learning. It covers a wide range of applications in agriculture, medical and multimedia systems, including drought prediction, health monitoring, event detection, and many more. The early classification aims to predict the class label of a time series as soon as possible without waiting for the complete

This work introduces a novel and more efficient image encryption scheme, that deploys Fresnelet diffraction in the wave-propagation domain in conjunction with image scrambling effect, based on a specific elliptic curve group, that substantially reduces the computational cost for the desired outcome. Significantly elevated security of the encrypted image is guaranteed by highly complex algebraic structure

Multi-Grouping Absolute Moment Block Truncation Coding (MGAMBTC) technique improve the image quality of Absolute Moment Block Truncation Coding (AMBTC) by adaptively dividing pixels in a block into groups according to block activity. This study found that the bit rate can be reduced further if there is some similarity among the blocks. Therefore, this study proposes a block prediction scheme that exploits

Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and global points are requires known as features. These features modified during the variation in human movement. But due to a bit change in several human actions, the features of these actions

This digital era uses lots of images in communication, which are confidential and large in volume. The transmission channel thus arises the question of security as well as transmission load. To date, several works are there to solve this issue. However, they fail to provide a single unit that gives these two facilities in a unified way. This paper presents a novel technique of unification of image

For the last many decades, the research is towards the classification of medical images in the early phase of its detection. But, the task becomes challenging due to the absence of the color information, like in natural scene images, and low illumination. In this paper, a multi-scale spectral approach is proposed for the classification of medical images. The proposed approach uses a dyadic filter bank

Nowadays, most of the traffic in vehicular networks is relevant to content, so the content-to-requester method could be an ideal solution for vehicular content delivery. However, due to the features of vehicular networks such as high mobility, the content-to-requester paradigm might cause the frequent content delivery failures and increase the content delivery cost. Inspired by the idea that the address-based

In this paper, to enhance the security of double random phase encoding (DRPE), Lorenz system and bidimensional empirical mode decomposition (BEMD) are introduced into an image encryption process. By means of BEMD, the original grayscale image is decomposed into three sub-images, then they are diffused and confused by using the pseudorandom sequences generated by Lorenz system. At last, the three result

Source Camera Identification is a well-known digital forensic challenge of mapping an image to its authentic source. The current state-of-the-art provides a number of successful and efficient solutions to this problem. However, in almost all such existing techniques, a sufficiently large number of image samples is required for pre-processing, before source identification. Limited labels classification

The arithmetic properties of a finite field have a remarkable impact on the security features of symmetric and asymmetric cryptosystems. Conventionally, in modern symmetric-key cryptosystems, a 256 elements Galois field depending on a single 8 degree primitive irreducible polynomial over ℤ2 is used for designing an S-box, the main nonlinear component in AES. The experience of S-box based image encryption

The latest generation of coding standard, Versatile Video Coding (VVC), has achieved more bitrate reduction compared with high efficiency video coding. However, the introduction of quadtree with nested Multi-Type Tree (MTT) coding structure greatly increases the computational complexity. To reduce the complexity of VVC, a Support Vector Machine (SVM) based Coding Unit (CU) size decision algorithm is

Face recognition is an efficient technique and one of the most preferred biometric modalities for the identification and verification of individuals as compared to voice, fingerprint, iris, retina eye scan, gait, ear and hand geometry. This has over the years necessitated researchers in both the academia and industry to come up with several face recognition techniques making it one of the most studied

The use of a binary classifier like the sigmoid function and loss functions reduces the accuracy of deep learning algorithms. This research aims to increase the accuracy of detecting and classifying oral tumours within a reduced processing time. The proposed system consists of a Convolutional neural network with a modified loss function to minimise the error in predicting and classifying oral tumours

Cryptography (encryption/decryption) is one of the prevailing mechanisms for protection of information (data or image) in the growing era of computerized exchange. It is the art of securing data/image by changing it into an unreadable format, called cipher text/image. Encryption mechanism is said to be efficient if it offers high security and robustness against attacks, and endow with very low correlation

Due to the development of artificial intelligence and computer vision technology, many autonomous drones have been studied. However, computer vision technology requires high performance CPU due to its high complexity, and battery consumption is so high that drones are constrained to fly for a long time. Therefore, low-power mobile devices require tracking algorithms that minimize battery consumption

The world is adopting complete wireless infrastructure to cover small size buildings as well as large geographical regions. Indoor Positioning System (IPS) is used to locate stationary as well as non-stationary objects in the wireless domain. The indoor localisation of a pedestrian remains an open problem in the noisy environment. Researchers are trying to develop low-complexity approach without depending

Despite enormous research efforts devoted by the research community to effectively and precisely perform video matching and retrieval among heterogeneous videos from large-scale video repositories still remains a complex and most challenging task. In order to address this complex challenge, a content based video retrieval technique is required, which can exploit the visual content of the videos for

Malware recognition has been widely used in the literature. One of the malware recognition methods is the byte code based methods. These methods generally use image processing and machine learning methods together to recognize malware. In this article, a novel byte code based malware recognition method is presented, and it consists of feature extraction using the proposed local neighborhood binary

Identifying a person based on their behavioral and biological qualities in an automated manner is called biometrics. The authentication system substituting traditional password and token for authentication and relies gradually on biometric authentication methods for verification of the identity of an individual. This proves the fact that society has started depending on biometric-based authentication

In this age of pervasive computing, numerous scientific accomplishments, such as artificial intelligence and machine learning [ML], have conveyed exciting uprisings to human civilization, which highlights the prospect to shape superior tools and solutions to aid to discourse some of the world’s utmost persistent challenges. Coding of English-dialectal dialogue recognition that has numerous datasets

Chronic Kidney Disease (CKD) is being typically observed as a health threatening issue, especially in developing countries, where receiving proper treatments are very expensive. Therefore, early prediction of CKD that protects the kidney and breaks the gradual progress of CKD has become an important issue for physicians and scientists. Internet of Things (IoT) as a useful paradigm in which, low cost

This paper is dedicated to investigating the application of blockchain in eHealth systems. Unlike the previous work, patients here play a supervisory role and allow medical institutions to legally use their medical data without prior authorization but own the right to manage the medical data. This concept is able to protect the rights and interests of patients without affecting the normal diagnosis

This paper presents automatic tumor detection and classification approaches for brain magnetic resonance images (MRI). These approaches are based on hybrid-optimized classification techniques and classify brain MRI to healthy, benign or malignant. The proposed system implements three-optimization techniques combined with Artificial Neural Network (ANN). Multi-Verse Optimizer (MVO), Moth-Flame Optimizer

Nowadays, most prevailing approaches to advanced audio coding (AAC) steganography are content non-adaptive which have low embedding capacity and poor security. In this paper, we construct a new distortion by integrating the uniform embedding distortion with the masking threshold, and propose an adaptive steganographic scheme for AAC audio by modifying quantized modified discrete cosine transform (QMDCT)

Facial recognition is a popular biometric technique to recognize an individual by comparing the facial features of a given photograph or a sketch to the digitally stored photographs. One of the important applications of facial recognition is to determine the identity of criminals through their hand-drawn or composite sketches. Despite of the development made in sketch-based facial recognition, available

In this paper, we propose HybridGAN – a new medical MR image synthesis methods via generative adversarial learning. Specifically, our synthesizer generates MRI data in a sequential manner: first in order to improve the robustness of image synthesis, an input full-size real MR image is divided into an array of sub-images. Then, to avoid overfitting limited MRI encodings, these sub-images and an unlimited

Reversible data hiding (RDH) is a data-hiding technique that embeds data into cover media such that it can be recovered distortion-free after the embedded data are retrieved. Currently, for RDH in encrypted halftone images (RDH-EH), the original cover image cannot be recovered once the watermark is extracted. In this paper, we present a RDH method for encrypted halftone images based on matrix embedding

With more and more color images are spread and shared in the untrusted cloud server remotely over the open Internet, some private or confidential information may be contained, it is necessary to develop cryptosystem for these images to free from serious threats. This paper investigates a secure and robust cryptosystem for color image based on discrete trinion Fourier transform and random-multiresolution

People with Color Vision Deficiency (CVD) cannot distinguish some color combinations under normal situations. Recoloring becomes a necessary adaptation procedure. In this paper, in order to adaptively find the key color components in an image, we first propose a self-adapting recoloring method with an Improved Octree Quantification Method (IOQM). Second, we design a screening tool of CVD datasets that

Ensemble clustering techniques have improved in recent years, offering better average performance between domains and data sets. Benefits range from finding novelty clustering which are unattainable by any single clustering algorithm to providing clustering stability, such that the quality is little affected by noise, outliers or sampling variations. The main clustering ensemble strategies are: to

Annotation-based image retrieval associates textual descriptions to images based on human perception. A user query, composed of keywords of choice and for retrieval, are usually matched lexically with the textual descriptions associated for stored images to extract the best matches. This paradigm will not produce appropriate desired results for complex queries if a semantic approach is not considered

Immunoglobulin A (IgA)-nephropathy (IgAN) is one of the major reasons for renal failure. It provides vital clues to estimate the stage and the proliferation rate of end-stage kidney disease. IgA stage can be estimated with the help of MEST-C score. The manual estimation of MEST-C score from whole slide kidney images is a very tedious and difficult task. This study uses some Convolutional neural networks

This paper presents a method of detecting abnormal activity in crowd videos while considering the direction of the dominant crowd motion. One main goal of our approach is to be able to run at the edge of the surveillance network close to the surveillance cameras so as to reduce network congestion and decision latency. To capture motion features while considering the direction of dominant crowd direction

Recently, a color image encryption scheme based on the Sine-Sine chaotic system has been proposed by Wang et al. (Signal Processing 144:444–452, 2018). In their scheme, a private parameter E is defined based on global plain information in hope of resisting the chosen-plaintext attack. However, we find that the private parameter is dependent purely on average intensity of a plain image, thereby leaving

As a competitive machine learning algorithm, extreme learning machine (ELM), with its simple theory and easy implementation, has been widely used in the field of pattern accuracy. Recently, researchers have proposed related research algorithms to accommodate noise and outlier data. With a proper fuzzy membership function, a fuzzy ELM can effectively reduce the effects of outliers when solving the classification

In this paper, a novel binary classifier termed as GPTSVM (projection twin support vector machine via Geometric Interpretation) is presented. In the spirit of original PTSVM, GPTSVM tries to seek two projection axes, one for each class, such that the projected samples of one class are well separated from that of the other class along its own projection axis. A pair of parameters (ν) are introduced

Recently, tracking methods based on correlation filter show impressive performance in a variety of complex environments for their excellent classification performance. However, most of the existing methods only focus on the information in the bounding box regions of the target, and do not fully utilized the contextual information and the internal structure information of the target. Thus, when the

Content based image retrieval (CBIR) is an extrusive technique of retrieving the relevant images from vast image archives by extracting their low level features. In this research paper, the pursuance of five most prominent texture feature extraction techniques used in CBIR systems are experimentally compared in detail. The main issue with the CBIR systems is the proper selection of techniques for the

In this paper, we present a word spotting system in text lines for offline Indic scripts such as Bangla (Bengali) and Devanagari. Recently, it was shown that the zone-wise recognition method improves word recognition performance than the conventional full word recognition system in Indic scripts, like Bangla, Devanagari, Gurumukhi (Roy et al. in Pattern Recogn 60: 1057-1075, 26; Bhunia et al. in Pattern

In the original publication, the corresponding author’s affiliation was incorrect. The correct affiliation is presented in this erratum. The author regrets this mistake.